What question did this study set out to answer?

To propose a dynamic licensing framework for effective runtime governance of autonomous AI systems.

April 7, 20260 citationsOpen Access

Agent Runtime Governance: Dynamic License Control for Autonomous AI Systems

Key Points

To propose a dynamic licensing framework for effective runtime governance of autonomous AI systems.
Presents AIBL Integrated Framework v2.5 as an extension of static licensing.
Formalizes a permission state space and a dynamic risk update function.
Introduces state transition rules and a conditional reinstatement model.
Establishes the need for a state-based permission lifecycle for agentic systems.
Demonstrates how dynamic licensing can provide better control over continuously operating AI agents.

Abstract

This working paper presents AIBL Integrated Framework v2.5, a theoretical extension of static licensing toward runtime governance for autonomous AI systems. The paper argues that agentic systems require a state-based permission lifecycle rather than one-time authorization or static consent. It formalizes three core elements: a permission state space, a dynamic risk update function, and a state transition rule, together with a conditional reinstatement model. The framework is intended as an institutionally plausible model of runtime permission control for continuously operating AI agents. It extends the logic of ReportingOS toward dynamic licensing, closed-loop agent governance, and incentive-compatible supervisory intervention. This version is released as a working paper for early dissemination and future development.

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